---
type: claim
domain: health
description: "The technology layer enabling $265B facility-to-home shift consists of RPM sensors generating continuous data processed through AI middleware to create actionable clinical insights"
confidence: likely
source: "McKinsey & Company, From Facility to Home report (2021); market data on RPM and AI middleware growth"
created: 2026-03-11
supports:
- home-based-care-could-capture-265-billion-in-medicare-spending-by-2025-through-hospital-at-home-remote-monitoring-and-post-acute-shift
reweave_edges:
- home-based-care-could-capture-265-billion-in-medicare-spending-by-2025-through-hospital-at-home-remote-monitoring-and-post-acute-shift|supports|2026-03-31
---

# RPM technology stack enables facility-to-home care migration through AI middleware that converts continuous data into clinical utility

The $265 billion facility-to-home care migration depends on a specific technology stack: remote patient monitoring sensors (growing 19% CAGR to $138B by 2033) generating continuous physiological data, processed through AI middleware (growing 27.5% CAGR to $8.4B by 2030) that converts raw sensor streams into clinically actionable insights. This architecture solves the fundamental problem that continuous data is too voluminous for direct clinician review—the AI layer performs triage, pattern recognition, and alert generation, enabling home-based care to achieve clinical outcomes comparable to facility-based monitoring.

The home healthcare segment is the fastest-growing RPM application at 25.3% CAGR, indicating that the technology has crossed the threshold from experimental to deployment-ready. With 71 million Americans expected to use RPM by 2025, the infrastructure for home-based care delivery is scaling faster than the care delivery models themselves.

## Evidence

- Remote patient monitoring market: $29B (2024) → $138B (2033), 19% CAGR
- AI in RPM: $2B (2024) → $8.4B (2030), 27.5% CAGR
- Home healthcare is fastest-growing RPM end-use segment at 25.3% CAGR
- 71M Americans expected to use RPM by 2025
- Hospital-at-home models achieve 19-30% cost savings while maintaining quality (Johns Hopkins)

## Technology-Care Site Coupling

This claim connects the technology layer ([[continuous health monitoring is converging on a multi-layer sensor stack of ambient wearables periodic patches and environmental sensors processed through AI middleware]]) to the care delivery site (home vs. facility). The AI middleware is not optional—it's the enabling constraint. Without AI processing continuous data streams, home-based monitoring generates alert fatigue and clinician overwhelm. With AI middleware, home monitoring becomes clinically viable at scale.

The atoms-to-bits conversion happens at the patient's home ([[healthcares defensible layer is where atoms become bits because physical-to-digital conversion generates the data that powers AI care while building patient trust that software alone cannot create]]), and the AI layer makes that data clinically useful ([[AI middleware bridges consumer wearable data to clinical utility because continuous data is too voluminous for direct clinician review]]).


### Additional Evidence (confirm)
*Source: [[2021-02-00-mckinsey-facility-to-home-265-billion-shift]] | Added: 2026-03-16*

McKinsey identifies RPM as the fastest-growing home healthcare end-use segment at 25.3% CAGR, with home healthcare specifically as the fastest-growing RPM application. The technology stack enables dialysis, post-acute care, long-term care, and infusions to become 'stitchable capabilities' that can shift home. COVID catalyzed permanent shift in care delivery expectations through telehealth adoption.

---

### Additional Evidence (extend)
*Source: [[2025-12-05-fda-tempo-pilot-cms-access-digital-health-ckm]] | Added: 2026-03-31*

TEMPO enables RPM deployment at the infrastructure level by providing both FDA enforcement discretion and CMS reimbursement for digital health devices targeting hypertension. However, this infrastructure is Medicare-only and research-scale (10 manufacturers), not a population-level deployment mechanism.


Relevant Notes:
- [[continuous health monitoring is converging on a multi-layer sensor stack of ambient wearables periodic patches and environmental sensors processed through AI middleware]]
- [[AI middleware bridges consumer wearable data to clinical utility because continuous data is too voluminous for direct clinician review]]
- [[healthcares defensible layer is where atoms become bits because physical-to-digital conversion generates the data that powers AI care while building patient trust that software alone cannot create]]

Topics:
- domains/health/_map